Dialysis patients have very high mortality rates with Cardiac Disease accounting for 43% of deaths. Data indicates that approximately 27% of the mortalities are due to Sudden Cardiac Death (SCD). SCD is unexpected death from a cardiac cause within a short time period, generally less than an hour from the onset of symptoms in a person without a prior condition that would appear to be fatal. In most cases, SCD death occurs because of Ventricular Arrhythmias (abnormal heart rhythms), including Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF). Moreover, Life Threatening Ventricular Arrhythmia (LTVA), specifically, is one of the most common causes of death among Chronic Kidney Disease (CKD) patients. CKD, also known as chronic renal disease, is a progressive loss in renal function over a period of months or years. Excessive fluid, ions and other toxins accumulate in patients with CKD. Although CKD patients are usually treated by hemodialysis therapy, the treatment is not continuous, but periodic, causing the build-up of excessive amount of fluids, electrolytes and waste in the body between hemodialysis sessions.
Although hemodialysis and pharmaceutical treatment reduces the concentration of elevated potassium in the blood and in the tissues in a patient, there is no current method to predict the onset of LTVA. As such, there is a need to detect an increase in the likelihood of a LTVA. There is also a need for a method to determine the onset of a life threatening cardiac event, so a patient can be prophylactically treated including by the administration of anti-arrhythmic drugs or additional dialysis. There is also a need for techniques to monitor various bodily functions and parameters such as tissue impedance that can than lead to better capabilities to predict LTVA in patients with cardiac disease, and/or in dialysis patients.
Arrhythmias are sometimes caused by the delivery of dialysis. This may be due to cardiovascular stress caused by fluid overload prior to dialysis, rapid changes in fluid or electrolyte levels during dialysis, or the rebalancing of fluid or electrolyte levels in a period shortly after dialysis. Hemodialysis patients have a rate of fatal arrhythmias that is 40 times greater than the general population. Although End Stage Renal Disease (ESRD) patients are at increased risk of arrhythmias and Sudden Cardiac Death (SCD), known systems do not relate data for the prevalence or likelihood of arrhythmias to a particular dialysis schedule in an on-going, dynamic, or patient-specific manner. Medical care in known dialysis settings fail to provide or respond to feedback obtained from sensors. Further, it is often unknown what happens after a patient has left the medical facility. Importantly, how a patient responds to particular course of dialysis is not collected, stored, analyzed, or associated with a patient medical record. Although physicians may collect pertinent data from disparate sources, the collected data is not obtained nor monitored by specially adapted dialysis computer systems and processors such as implanted ECG and impedance sensors. Moreover, known systems do not monitor physiological data pre- or post-dialysis. Instead, blood pressure or heart rate monitoring is limited to 30-minute intervals during treatment.
Known dialysis systems and methods of treatment provide a fixed schedule of dialysis with little to no monitoring of related medical parameters, and consequently, no adjustment of dialysis parameters. Known dialysis systems also do not collect ECG data when a patient is not inside a hospital setting. Known systems also fail to provide prevalence of arrhythmias data in relationship to a particular set of dialysis parameters in synchronized form. Instead of providing a precise dialysis schedule that responds to observed arrhythmia events, most dialysis is prescribed in advance with little to no computer assisted monitoring. As such, known dialysis systems and methods are incapable of providing personalized care keyed to on-going feedback obtained from a patient.
Known dialysis systems further fail to incorporate data obtained from implantable medical devices (IMDs), such as implantable dialysis devices, pacemakers, drug delivery devices, ILRs, blood panels, a micro-fluidics based ambulatory blood composition monitor, or other devices that can monitor and record medical information from patients, such as the occurrence of arrhythmia, heart rate, or blood pressure. As such, the known systems and methods cannot determine the effect of such obtained data and thereby cannot improve therapy nor correctly align the monitored data with the periodic occurrences of dialysis for further computation and analysis. Known IMDs are not capable of generating a report tailored to show the relationship between changes in the medical data and the occurrence of dialysis for a specific patient. Known systems also cannot monitor at-home dialysis systems. Known monitoring systems do not provide a method to generate reports showing a relationship for long periods before and after dialysis for at-home systems.
Hence, there is a need for a personalized dialysis system that can present and analyze data to optimize dialysis treatment. There is also a need for obtaining, monitoring, and presenting patient data in an on-going, dynamic, or patient-specific manner. The need extends to providing a display for showing a relationship between data from at least one dialysis session parameter and data from at least one medical parameter. The need extends to systems and methods configured for monitoring patients when they are not under direct observation in a hospital or medical care setting. The need includes collecting, analyzing, and displaying inter-session data between dialysis visits. The need further includes configurations for monitoring and transmitting such data for at-home patients.
There is also a need for a medical monitoring system that can simultaneously provide the monitored medical data and show the occurrence of dialysis sessions so that health care personnel can properly interpret the changes in patient health due to dialysis, thereby enabling changes in dialysis treatment in order to avoid unwanted medical issues. There is further a need for a medical monitoring system that can provide displayed reports configured to the needs of medical professionals or researchers for interpreting the effects of dialysis on medical parameters.
There is a need for monitoring physiological data pre- or post-dialysis. There is further a need for providing a precise dialysis schedule that responds to observed arrhythmia events using specially adapted computers and dialysis systems suitable for the requirements of a healthcare setting including patient privacy. There is a need for dialysis systems and methods capable of providing personalized care based on data obtained from a patient. The dialysis systems and methods should incorporate data obtained from implantable medical devices (IMDs), such as implantable dialysis devices, pacemakers, drug delivery devices, ILRs, blood panels, a micro-fluidics based ambulatory blood composition monitor, or other devices that can monitor and record medical information from patients, such as the occurrence of arrhythmia, heart rate, or blood pressure. The need extends to correctly aligning therapy parameters based on the monitored data using specially adapted computers that address the technical challenges extant in healthcare settings such as patient safety and privacy.